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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Nauman, Nasir Mustafa, Hirra Ashraf, Abrar Khalil, Hisham Department of Computer Science Superior University Lahore Pakistan Department of Computer Science Sharif College of Engineering and Technology Lahore Lahore Pakistan Embedded System and Robotics Lab Department of Technology The University of Lahore Lahore Pakistan

ISBN: (纸本)9783031776199

This paper presents the design of a system onboard spider robot for rescue services. The spider has the six-leg walking pattern that contains the total number of 18 servo motors. In order to reduce the payload, the abdomen of the spider is made with RF4 printed circuit board (PCB). Whereas, the abdomen is also used as the main structure of the robot to attach the rest of the system. Wherein, two pulse-width modulations (PWM) module are controlled by Arduino Nano, the Bluetooth module is used for communication link and bulk converter for the power supply. The spider robot is operated with an in-house developed Android application. It has been observed that the spider robot has a stable walking response along with the multiple excise performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine design

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Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles 59

Generative AI-Enhanced Cooperative MEC of UAVs and Ground St...

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59th Annual Conference on Information sciences and systems, CISS 2025

作者： You, Jiahao Jia, Ziye Dong, Chao Wu, Qihui Han, Zhu Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

ISBN: (纸本)9798331513269

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios. © 2025 IEEE.

关键词： Unmanned surface vehicles

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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy 23

ACGraph: Accelerating Streaming Graph Processing via Depende...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

ISBN: (纸本)9798350323481

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

关键词： graph processing

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Reducing interdataset covariate shift in sleep EEG of traumatic brain injured humans and mice using Transfer Euclidean Alignment

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Biomedical Signal Processing and Control 2025年 108卷

作者： Vishwanath, Manoj Cao, Steven Dutt, Nikil Rahmani, Amir M. Lim, Miranda M. Cao, Hung Department of Computer Science University of California IrvineCA United States Department of Electrical Engineering and Computer Science University of California IrvineCA United States VA Portland Health Care System PortlandOR United States Oregon Health & Science University PortlandOR United States

While the analysis of sleep electroencephalography (EEG) offers distinct advantages over other methods in clinical applications, high variability across subjects poses a significant challenge in deploying machine learning (ML) models for real-world classification tasks. In such instances, ML models that exhibit exceptional performance on a dataset may not necessarily demonstrate similar proficiency when applied to a different dataset performing the same task. The scarcity of high-quality biomedical data further exacerbates this challenge, making it difficult to comprehensively evaluate the model's generalizability. In this paper, we introduce Transfer Euclidean Alignment — a transfer learning (TL) technique designed to tackle the problem of the dearth of human biomedical data in training deep learning (DL) models. We test the robustness of this TL technique across various ML models as well as the EEGNet-based DL model, by evaluating them on different datasets, including human and mouse data, for the binary classification task of detecting individuals with versus without traumatic brain injury (TBI). This work represents the first demonstration of the use of TL in the context of utilizing a mouse model as the source data to improve the performance of the target human dataset. By demonstrating notable improvements with an average increase of 14.42% for intraspecies datasets and 5.46% for interspecies datasets, our findings underscore the importance of TL in boosting the performance of ML and DL models trained on diverse datasets. © 2025 The Authors

关键词： Electroencephalography

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FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph Convolutional Network for Traffic Forecasting

FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinpeng Xu Chunna Zhao Jing Yang Yaqun Huang Yaoyuan Yang Por Lip Yee School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data; 2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model.

关键词： Accuracy Convolution Urban areas Time series analysis Spatial databases Spatiotemporal phenomena Sensors Speech processing Spatial resolution Signal resolution

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An explainable multi-objective hybrid machine learning model for reducing heart failure mortality

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PeerJ computer science 2025年 11卷 e2682-e2682页

作者： Shamrat, F.M. Javed Mehedi Khalid, Majdi Qadah, Thamir M. Farrash, Majed Alshanbari, Hanan Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer Science and Artificial Intelligence Umm Al-Qura University Makkah Saudi Arabia Department of Computer and Network Engineering Collge of Computing Umm Al-Qura University Makkah Saudi Arabia

As the world grapples with pandemics and increasing stress levels among individuals, heart failure (HF) has emerged as a prominent cause of mortality on a global scale. The most effective approach to improving the chances of individuals’ survival is to diagnose this condition at an early stage. Researchers widely utilize supervised feature selection techniques alongside conventional standalone machine learning (ML) algorithms to achieve the goal. However, these approaches may not consistently demonstrate robust performance when applied to data that they have not encountered before, and struggle to discern intricate patterns within the data. Hence, we present a Multi-objective Stacked Enable Hybrid Model (MO-SEHM), that aims to find out the best feature subsets out of numerous different sets, considering multiple objectives. The Stacked Enable Hybrid Model (SEHM) plays the role of classifier and integrates with a multi-objective feature selection method, the Non-dominated Sorting Genetic Algorithm II (NSGA-II). We employed an HF dataset from the Faisalabad Institute of Cardiology (FIOC) and evaluated six ML models, including SEHM with and without NSGA-II for experimental purposes. The Pareto front (PF) demonstrates that our introduced MO-SEHM surpasses the other models, obtaining 94.87% accuracy with the nine relevant features. Finally, we have applied Local Interpretable Model-agnostic Explanations (LIME) with MO-SEHM to explain the reasons for individual outcomes, which makes our model transparent to the patients and stakeholders. Copyright 2025 Shamrat et al. Distributed under Creative Commons CC-BY 4.0

关键词： Heart

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DSAA: Cross-modal transferable double sparse adversarial attacks from images to videos

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Neurocomputing 2025年 639卷

作者： Han, Xueqiang Zhang, Shihui Wang, Houlin Tian, Qing School of Information Science and Engineering Yanshan University Hebei Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Hebei Qinhuangdao China Department of Computer Science University of Alabama at Birmingham AL United States

Recent preliminary research has examined cross-modal attacks, enabling the transition of unlabeled attacks from images to videos. The current cross-modal attacks are mainly dense attacks, while sparse attacks have not been explored. To bridge this gap, this paper proposes a cross-modal transferable double sparse adversarial attack (DSAA) method. Specifically, DSAA incorporates both spatial sparsity and temporal sparsity. Firstly, we design a novel double-cross-shaped patch paradigm to achieve spatial sparsity. Leveraging this patch for perturbation addition enables a more effective global impact on video frames. Subsequently, we propose a uniform temporal sparsity strategy to achieve temporal sparsity. Even if only half of the frames are perturbed, the resulting average attack success rate remains comparable to that of perturbing all frames. Furthermore, we take into account the luminance, contrast, and structure of images and propose a structural similarity loss function (SSLF). By utilizing SSLF to measure the similarity of intermediate feature maps and incorporating it into perturbation optimization, DSAA achieves excellent attack performance. Extensive experiments demonstrate DSAA's superiority over state-of-the-art approaches in terms of attack success rate. The proposed DSAA uses video adversarial examples generated by four ImageNet-pretrained image models on Kinetics-400 and UCF-101 to attack six video recognition models, achieving average attack success rates of 85.83% and 82.02%, respectively. © 2025 Elsevier B.V.

关键词： Cross-modal attacks Spatial sparsity Temporal sparsity Unlabeled attacks Video adversarial examples

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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Damage-Free Neutral Beam Etching for Gate Recess in E-Mode AlGaN/GaN HEMTs

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 705-708页

作者： Chen, Yi-Ho Chu, Fu-Chuan Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung-Shan Institute of Science and Technology Solid State Devices Section Electronic System Research Division Taoyuan325 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Recess gate etching is a critical technique for achieving enhancement-mode (E-mode) AlGaN/GaN high-electron mobility transistors (HEMTs) because the interface is susceptible to the etching damage. This study fabricates recess gates using the neutral beam etching (NBE) technique. By adjusting the aperture thickness in the NBE apparatus, we simulate both NB-mode and plasma-mode etching. The electrical characteristics of E-mode HEMTs fabricated using these two modes are analyzed and compared through DC, noise, and pulsed IV measurements. The results demonstrate that NB-recessed HEMTs exhibit superior performance. © 1980-2012 IEEE.

关键词： Aluminum gallium nitride

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Edge AI Robot Technology Application with YOLOv8, MQTT and MQ-5 Sensors for Realtime Fire and Smoke Detection 14th

Edge AI Robot Technology Application with YOLOv8, MQTT and...

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14th International Conference on Frontier Computing, FC 2024

作者： Andriyadi, Anggi Wijaya, Chandra Chen, Shih-Yen Huang, Ding-Hsiang Yang, Chao-Tung Department of Industrial Engineering and Enterprise Information Tunghai University Taichung City Taiwan Department of Computer Science Tunghai University Taichung City Taiwan Department of Information System Institut Informatika dan Bisnis Darmajaya Bandar Lampung Indonesia Department of Informatics Universitas Katolik Parahyangan Bandung Indonesia Research Center for Smart Sustainable Circular Economy Tunghai University Taichung City Taiwan

ISBN: (纸本)9789819623945

Fire incidents pose a significant security threat that requires attention from all stakeholders. For example, Taipei reported 893 fire incidents in 2023 alone. This study aims to enhance security surveillance by promptly detecting fire incidents through the development of a patrol robot. The robot captures sensor signals from the MQ-5 and transmits them via MQTT to the Raspberry Pi control center, delivering real-time reports of smoke and fire to fire officials. Additionally, equipped with a camera, the robot streams footage using RTSP to Jetson for fire and smoke object recognition using the You Look Only Once (YOLO) algorithm. Our robot effectively detected smoke and fire using the MQ5-Sensor and transmitted the signals via MQTT. Furthermore, it analyzed the visual stream to identify fire and smoke patterns with YOLOv8, resulting in a significant increase in accuracy. The evaluation of our model yielded impressive performance metrics, including a precision of 93%, a recall of 97%, a precision-recall of 97.90%, and an F1-Score of 97%. In the future studies we will integrate a mini camera and Large Language Model (LLM) to enable the robot to communicate with humans to warn of early fire incidents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smoke detectors

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